Features. We use the video features extracted by TBN [1]}. Each one is an nx25x1024 matrix holding a python dictionary containing the RGB, flow, and audio features, where n is the number of video clips. The number of training and test set pairs is 67217 and 9668, respectively. Each feature is followed by temporal mean pooling, making the shape nx1x1024. We utilize the textual features given by [2]} using a Word2Vec model trained on the Wikipedia corpus. spaCy parser [3]} is used to disentangle the text caption into different PoS tags. The model is trained with the default values of the baseline.
